For the past few months I’ve been collaborating with Jack Iwashyna, Assistant Professor at UofM’s medical school and SI MSI student Umanka Hebbar Karkada. Jack had a fun idea, and a fun data set – hospital to hospital patient transfers, mined from medicare claims. These transfers are a way for highly resistant infections to jump from one critical care unit to another. Mostly hospitals devote resources to preventing infection spread separately from one another.
We posed the question of how resources could be allocated in a coordinated way to maximally stem the spread of infection. Umanka and I tried several stategies – targeting hospitals with the highest degree (number of hospitals they trade patients with), highest betweenness (they are on the “path” between other hospitals), and a greedy allocation based on the number of beds infected at each hospital and downstream from that hospital.
The results are here. Both figures show hospitals as nodes sized by the number of ICU beds they have.
This shows the number of resources allocated by hospital (gray = none, blue = few, red = many).
This shows the relative benefit of a random allocation vs. targeting particular hospitals. (blue = hospital unlikely to become infected, red = likely to be infected)

Xiaolin Shi will be defending her thesis on June 3rd, 2:00 – 4:00 PM, in CSE 3725 (the computer science building). She’s the first student of mine to have reached this stage, and so I’m experiencing a bit of anxiety, though she is quite ready. Next up for her is a postdoc with Dan MacFarland at Stanford (she’ll be part of an interdisciplinary team of computer scientists, linguists, and education-folk to study how the education environment impacts future scholarly performance).
Info on her thesis:
THE STRUCTURE AND DYNAMICS OF INFORMATION SHARING NETWORKS
Information flows are produced, carried, and directed by information sharing networks. And the evolution of the structure of such networks and the way information diffuses are affected by one another. This thesis studies structural features of information networks and their relationships to information diffusion…

At ICWSM I saw a nifty demo of gephi. Lots of clickable UI-type things. Some cool features I saw:
* easily subsetting nodes according to attributes
* getting node labels to jiggle around until they no longer overlap
* drawing curved arcs (and controlling the curvature)

Eytan, Brian and I have a paper at EC (to be presented by Eytan @ Stanford in July).
It’s on the diffusion of gestures in Second Life (the online massively-multiplayer virtual world). The neat thing about SL users passing assets around is that it leaves digital traces. We were able to surmise that roughly half of the transfers occur between friends (according to the explicit social graph), and that in 38% of the remaining cases a user adopts after their a friend does. Not only that, but as more friends adopt, the hazard of adopting increases. Transfers along the social network are faster, but the overall spread is more limited… Influencers and adopters are distinct groups… What else? Well, you’ll have to read the paper .